How to Adapt to IoT Devices and AI as a Software Professional
When something about your job changes—especially if you’ve been doing it the same way for a long time—it’s easy to panic. Instead of crafting a smart game plan to deal with the new situation, we often overreact out of frustration or even fear of change.
This applies to the Internet of Things and artificial intelligence, which are seemingly transforming the way we develop, test, and think about software. With every little thing you own having a connection to the Internet and bots/automation doing a lot of the heavy lifting when it comes to testing, it’s easy to see how professionals in the industry might feel they need to change everything to adapt.
However, Jonathon Wright, the director of software engineering at CA Technologies, recently spoke at STARWEST about how you shouldn’t discount all your current skills or fear you’ll have to be retrained just to continue testing and developing software.
The best part about IoT and AI is that what you’re doing now will transfer over.
“Don't panic. Digital is going to change things, but now, those challenges are going to be the same as what you did before,” Wright explains. “You'll still be using classification trees. The difference is, you'll be applying them in a sense of testing an IoT platform or a new banking API. You have to still use your skills.”
Wright understands that people will question how to test something that can't be physically seen or wonder how they’ll train AI to use huge amounts of data in a big data set scenario. But just remember: You’re still going through the same testing steps. When using automation or AI, you’re still programming it to do the work you’ve done in the past, just with a deeper pool of data.
Have confidence in what you know. Look at IoT and AI as new platforms to work with, not entirely new professions you’ll have to learn.
“If you take it down to just nodes, it's very simple. Whether that node's an IoT device or sensor, or it's just an API, it doesn't matter,” Wright continued. “Part of it is you're proving a path through an application with some data and you're getting confidence. The more you learn, the more you repeat through there with different sets of test data and try different negative exhaustive testing mechanisms. You're getting more confident.”